Directional drilling is a challenging task even during the best of conditions, with many aspects described as more art than science. Successful and cost-effective directional drilling often comes down to an expert who has a good feel of how to navigate thousands of feet underground, site unseen. Therefore, the art of drilling often comes down to the artists, who are in high demand and short supply.
To address this scarcity, improve the economics and deliver consistently better outcomes, Oceanit was challenged to develop an artificial intelligence (AI)- driven system that performed as well as the experts and could augment or replicate their capabilities. The result is a system that, so far, delivers results within a 1.5% margin of expert drillers. Oceanit’s AI drilling system is playfully referred to as “Deep Thought,” a reference to the fictional computer in The Hitchhiker’s Guide to the Galaxy.
Deep Thought is capable of continuously learning and improving on its directional drilling decision-making. The scalable capabilities it provides can significantly reduce costs for operators while increasing high-performance outcomes through its reinforcement learning, replication and optimization of drilling mechanics.
Minimizing deviation, tortuosity
Working with Shell International Exploration, Oceanit developed Deep Thought to optimize value in directional drilling by minimizing deviation from planned wellbore trajectory, minimizing tortuosity, maximizing the ROP and reducing the number of personnel onboard, all factors that have serious impacts on the bottom line.
With directional drilling, expert teams maintain a stationary drillstring at the surface to achieve a curved hole. There are two main categories of systems used in directional drilling: bent-sub downhole motors and rotary steerable systems. Oceanit focused on the prior, as bent-sub downhole motors are generally more costeffective and prevalent in shale plays.
It is incredibly challenging to control the angular orientation of the drillbit toolface while ensuring adequate ROP. Eliminating trajectory deviations and the associated, costly corrective measures are imperative for improved outcomes. To reduce deviations, Oceanit used machine learning techniques in training Deep Thought to replicate the decisions of expert drillers.
A multilayer perceptron neural network is a class of ANNs that uses a supervised learning technique called backpropagation for training and can distinguish data that are not linearly separable.
Source:epmag
